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https://github.com/lucadibello/concurrency-spin-model-checker

This project showcases the integration of "SPIN" model checker to verify the correctness of concurrent programs
https://github.com/lucadibello/concurrency-spin-model-checker

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This project showcases the integration of "SPIN" model checker to verify the correctness of concurrent programs

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# Model checking with SPIN



## Introduction



This assignment examines the implementation of model checking using the

[SPIN](https://spinroot.com/spin/whatispin.html) tool to verify the

correctness of two versions of a frequency counter program: one

sequential and the other parallel. Model checking is a technique that

allows to verify the correctness of certain properties of a system

described in a finite-state model.



The following sections will discussed how the program has been modeled

using [ProMeLa](https://en.wikipedia.org/wiki/Promela) language, which

Linear Temporal Logic (LTL) properties have been defined to verify the

correctness of the program, and how the verification has been performed

using SPIN.



## ProMeLa Model



The ProMeLa model consists of two main processes: the first process

handles the sequential computation of frequency counts, storing results

in an array `sequential_counts`. The second process on the other hand,

initiates parallel computation by spawning a worker process for each

possible value in the input array. These workers update an array

`parallel_counts` concurrently without encountering race conditions as

each worker updates a unique position in the array.



As explicitly stated in the assignment, the ProMeLa model presents two

constrants:



1.  `MAX`: It represents the maximum value that can be assigned to an

    element in the array. This constant is used as an upper bound when

    filling the input array with random values.



2.  `LENGTH`: the length of the input array. This constant is used many

    times in the model to iterate over the input array while performing

    various operations.



The model presents an `init` block that initializes the input array with

random values between 0 and `MAX` and starts both the sequential and

parallel processes. The code is available in code block below:



```promela

//

// ProMeLa code

//



// Define the maximum number of elements in the array

#define MAX 2

#define LENGTH 2



// Define the variables

int a[LENGTH];



// Keep track of result of both versions of the program

int sequential_counts[MAX + 1];

int parallel_counts[MAX + 1];



// Entry point of the program

init {

    // Initialize the array non-deterministically

  printf("Random state:\n")

    int i;

    for (i : 0 .. LENGTH - 1) {

        // Select a random value for the array

        int v;

        select(v : 0 .. MAX);

        // Assign the value to the array

        a[i] = v;



    // Print the value

    printf("\ta[%d] = %d\n", i, v);

    }



    // Run the sequential version of the program

  printf("Running sequential version...\n");

    run sequentialCounter();



  // Run the parallel version of the program

  printf("Running parallel version...\n");

  run parallelCounter();

}

```



From the code above is possible to see that both versions of the program

have been started using the `run` keyword inside the same ProMela model.

This is different from the Java implementation, where the two versions

were implemented in separate classes. This design choice was essential

to allow the verification of both versions of the program in the same

model; otherwise, it would be impossible to perform verifications that

compare data yielded by two different simulations.



In the abstraction, the sequential and parallel processes are

implemented as separate processes. An in-depth analysis of each process

is provided in the following sections.



### Sequential Process



The sequential version of the frequency counter program is implemented

in the `sequentialCounter` process. This process iterates over the input

array and increments the corresponding position in the

`sequential_counts` array.



The ProMeLa implementation of this version of the program is almost

identical to the Java implementation. This was expected, as both

languages use a C-like syntax, and the logic of the program is simple.

The code is available in listing blow:



```java

//

// Java code

//

public int mostFrequent() {

    int mostFrequent = -1;

    int maxFrequency = -1;

    int[] frequencies = new int[max + 1];

    for (int k = 0; k < a.length; k++) {

        int value = a[k];

        frequencies[value] += 1;

        int frequency = frequencies[value];

        if (frequency > maxFrequency) {

            mostFrequent = value;

            maxFrequency = frequency;

        }

    }

    return mostFrequent;

}

```



```promela

//

// ProMeLa code

//

proctype sequentialCounter() {

    int maxFrequency = -1;

    int k;

    for (k : 0 .. LENGTH - 1) {

        int value = a[k];

        sequential_counts[value] = sequential_counts[value] + 1;

        if

        :: sequential_counts[value] > maxFrequency ->

            maxFrequency = sequential_counts[value];

            sequential_result = value;

        :: else -> skip;

        fi

    }

    // Signal that the sequential version is done

    sequentialDone = 1;

}

```



The main difference between the Java and ProMeLa implementations is the

way the program yields the result: in the Java implementation, the

result is simply returned by the method, while in the ProMeLa

implementation, the result is stored in a global variable

`sequential_result`. This is necessary as the process cannot return a

value.



### Parallel Process



The parallel version of the frequency counter program is implemented in

the `parallelCounter` process. This process spawns `MAX + 1` worker

processes that concurrently count the frequency of each value in the

input array. Each worker process is started with a unique value to

count, and by iterating over the input array, increments the

corresponding position in the `parallel_counts` array.



After all worker processes have completed, the main parallel counter

process iterates through the results and saves the value with the

highest frequency inside the `parallel_result` variable.



To detect when all worker processes have completed, a _channel_ is used

to synchronize the main process with the workers. The channel is created

in the main process and passed as an argument to each worker process. As

soon as a worker process completes, it sends its PID through the

channel. Since we start `MAX + 1` worker processes, we expect to read

`MAX + 1` values from the channel. By leveraging this, the

_parallelCounter_ process can detect when all worker processes have

completed. The code is available in code block below.



```promela

//

// ProMeLa code

//

proctype parallelCounter() {

  // Create channel to wait for workers to finish

  chan joinCh = [MAX + 1] of { pid };

  // Create array of PID's for workers

  pid workers[MAX + 1];

  // Create a worker for each possible value

  int i;

  for (i : 0 .. MAX) {

    workers[i] = run parallelWorker(i, joinCh);

  }

  // Wait for all workers to finish by reading from the channel

  for (i : 0 .. MAX) {

    int done;

    joinCh ? done;

  }

  // If we are here, all workers are done as we read MAX+1 values from the channel

  printf("\t [!] all workers are done\n");

  ...

}

```



This implementation differs from the Java implementation, where it does

not wait for all threads to complete but instead starts them all

together and then waits for each thread individually to complete and

save its result. In the ProMeLa abstraction, we start the worker

processes right away and use a channel to wait for all of them to

complete before saving the actual result.



#### Worker Process



The worker process has been implemented in the `parallelWorker` process.

As mentioned in the previous section, each worker process is started

with a unique value to count and increments the corresponding position

in the `parallel_counts` array when it finds the value in the input

array. The code is available in code block below.



The main difference between the two implementations is how the worker

process is started. In the Java implementation, this required the

creation of a specific class that implements the `Runnable` interface,

while in the ProMeLa implementation, this each worker is a process that

is started by the main `parallelCounter` process.



```java

//

// Java code

//

protected int frequencyOf(int n) {

    int frequency = 0;

    for (int value: a) {

      if (value == n)

        frequency += 1;

    }

    return frequency;

}



class ThreadedCounter extends SequentialCounter implements Runnable

{

     private int frequency;

     private int n;



     ThreadedCounter(int[] a, int n, int max) {

          super(a, max);

          this.n = n;

     }



     public void run() {

          frequency = frequencyOf(n);

     }



     public int frequency() {

          return frequency;

     }

}

```



```promela

//

// ProMeLa code

//



// The worker process represents a thread that looks for the count of a specific value in the array

proctype parallelWorker(int value; chan out) {

  // Look for the value in the array

  int frequency = 0;

  int i;

  for (i : 0 .. LENGTH - 1) {

    if

      :: a[i] == value -> frequency = frequency + 1;

      :: else -> skip;

    fi

  }

  // Update the value in the parallel counts array

  parallel_counts[value] = frequency;

  printf("Worker for value %d is done\n", value);

  out ! _pid;

}

```



A notable implementation detail is that the Java worker, using an

external function named _frequencyOf_, only computes the frequency of a

specific value. In contrast, the ProMeLa worker process not only

computes this frequency but also saves the result in the

`parallel_counts` array and synchronizes with the parent process using

the channel.



### Parameters: MAX and LENGTH



If MAX and LENGTH are too high, the model checking process can take a

long time to complete, or even run out of memory. To analyze the

behavior of the model checking process with different values of MAX and

LENGTH, I first run the checker by keeping one parameter fixed and

varying the other. This has been done for both parameters to understad

their impact on the model checking performance. The results can be seen

in the following figure:



![Model checking execution time with different values of MAX and

LENGTH](./report/images/max_length_exec_time.png)



Then, to have a better understanding, I also decided to increase the

values of both MAX and LENGTH to understand how the model checking

process behaves. Then, I merged the results in a single plot, available

in the figure below:



![Model checking execution time with increasing values of MAX and

LENGTH](./report/images/max_len_increase_exec_time.png)



From the plot, it is possible to see that the model checking process is

fast enough when the values of MAX and LENGTH are extremely low

($\leq 2$). However, as the values of MAX and LENGT increase, the model

checking process becomes explonentially slower. This is expected, as the

model checking process has to explore all possible states of the system,

and the number of states grows exponentially with the values of MAX and

LENGTH.



After the benchmarks outlined above, has been decided to set the valued

of MAX and LENGTH to 2, to keep the model checking process fast and

efficient enough to be able to analyze the behavior of the model.



## LTL Properties



In the following subsections, I will discuss the LTL properties that

have been defined to satisfy the requirements of the assignment. I

developed



### Verify completition of both sequential and parallel processes



To be able to verify the completition of both the sequential and

parallel processes, I decided to use two global variables:

`sequentialDone` and `parallelDone`. These variables are set to 0 by

default, and set to 1 when the respective process has completed. To

verify the completition of both processes, I defined the following LTL

property:



```promela

ltl termination { <> (sequentialdone == 1 && paralleldone == 1) }

```



### Same most frequent value



To ensure that both versions of the frequency counter give the same

result, I defined the following LTL property:



```promela

ltl sameResult { [] (sequentialDone == 1 && parallelDone == 1) -> (sequential_result == parallel_result)}

```



This LTL verifies that the result of both versions (sequential and

parallel) is the same after both processes have completed. This is

important property to verify, as the two algorithms are expected to

yield the same result.



### Sum of frequencies



As we are interested in verifying the correctness of the frequency

counter program, I decided to add an additional LTL properety that

ensures that the sum of the frequencies of all values in the

`sequential_counts` and `parallel_counts` arrays is equal to the length

of the input array. This property is defined as follows:



```promela

ltl sumCounts { [] (sequentialDone == 1 && parallelDone == 1) -> (sumCountsSequential == LENGTH && sumCountsParallel == LENGTH) }

```



To verify this property I had to add two \"counter\" variables,\

`sumCountsSequential` and `sumCountsParallel`, that are computed by

summing the frequencies of all values in the respective arrays right

before completing the respective processes. This way, I can verify that

the sum of the frequencies is equal to the length of the input array.

Without these variables, it would be impossible to verify the total sum

of the frequencies.



### Invalid LTL formula: partial result



As requesed by the assignment, I also added on purpose an invalid LTL

formula that SPIN will not be able to verify. The property I decided to

add is the following:



```promela

ltl alwaysSameResult { [](sequential_result == parallel_result) }

```



This property is very similar to the `sameResult` property,

but it does not take into account the completition of the processes.

This LTL is wrong, as the two results are

not expected to be the same while the processes are still running.



This is because the two versions of the program are not executed in

parallel (first the sequential version and then the parallel version)

and also, the two versions work in different ways. The sequential

version computes the result and updates the `sequential_result` variable

as soon as it finds a value with a higher frequency. The parallel

version, on the other hand, spawns multiple worker processes that

concurrently compute the frequency of each value in the input array,

and, only after all workers have completed, the main process updates the

`parallel_result` variable.



For these multiple reasons, the `alwaysSameResult` property is invalid

and SPIN will find a counterexample right after the first iteration of

the sequential process. This is the counterexample that SPIN will find

(truncate for brevity):



```text

    #processes: 2

                    a[0] = 0

                    a[1] = 0

                    sequential_counts[0] = 1

                    sequential_counts[1] = 0

                    sequential_counts[2] = 0

                    parallel_counts[0] = 0

                    parallel_counts[1] = 0

                    parallel_counts[2] = 0

                    sequential_result = 0

                    parallel_result = -1

                    sequentialDone = 0

                    parallelDone = 0

                    sumCountsSequential = 0

                    sumCountsParallel = 0

     41:    proc  1 (sequentialCounter:1) ../src/promela/model.pml:51 (state 12)

     41:    proc  0 (:init::1) ../src/promela/model.pml:42 (state 22)

     41:    proc  - (notSameResult:1) _spin_nvr.tmp:60 (state 6)

```



From the output above, it is possible to understand why, where and when

SPIN found the counterexample: the LTL property is violated right after

the first iteration of the sequential process as the two results are

different (`sequential_result` = 0 and `parallel_result` = -1). At the

moment ot the violation, the parallel process has not even started yet.



## SPIN model checker via script



To automate the verification process, has been created a script that

will build the ProMeLa model, compile the resulting analyzer using

`gcc`, and run the model checker using SPIN for each of the defined LTL

properties. To run the script, it is possible to use the

provided Makefile target `run` or run the script directly. Use the

commands below to run the script:



```bash

      make run

      # or

      ./scripts/run-model.sh

```